Industry foundations

A field guide to how models improve.

Start with the lifecycle. Each module links technical ideas to the data, people, and infrastructure required to make them work.

1 module · Last reviewed 2026-07-13

Lifecycle overview

Four stages, one feedback loop.

These categories simplify a complicated process, but they provide a stable map for understanding where companies and methods fit.

01
Pretraining
Build a base model from broad data and predictive objectives.
02
Post-training
Shape behavior with demonstrations, preferences, outcomes, and practice.
03
Evaluation
Measure capabilities, trade-offs, and failures under defined conditions.
04
Inference & operation
Run the model in products or agent systems with tools and environments.

Text equivalent: pretraining builds the base model; post-training shapes behavior; evaluation measures results; and inference or agent operation applies the system. Evidence from evaluation and operation feeds new data, tasks, training, and tests, so these stages can overlap and repeat.

Modules

Begin with the system, then zoom in.

Module 01

From pretraining to agents: how the AI model-development lifecycle fits together

A system-level guide to how base models become instruction-following, evaluated, tool-using systems—and how evidence from operation feeds the next cycle.

Four stages, one feedback loop.

Pretraining

Post-training

Evaluation

Inference & operation

Begin with the system, then zoom in.

From pretraining to agents: how the AI model-development lifecycle fits together